Operations | Monitoring | ITSM | DevOps | Cloud

Did you know the third week of January is the most common time for people to fail New Year’s Resolutions? It doesn’t matter whether it’s exercising more, learning a new language, or just trying to drink less coffee, that initial surge of fresh New Year’s energy is fading, and if you want to make a resolution stick, this is the key time to make a lasting change. The same is true with any reliability resolutions you might have made.

This fall and winter have had their share of major outages (including AWS, Azure, and Cloudflare), and December was no exception. On December 5, 2025, Cloudflare suffered a 25-minute outage that served responses with HTTP 500 errors to about 28% of HTTP traffic served by Cloudflare. Since Cloudflare handles an average of 81 million HTTP requests per second, this represents a substantial chunk of internet traffic, including LinkedIn, Zoom, and Downdetector.

2025 was a stark reminder of why reliability is so critical in the tech sector. The year wrapped up with multiple high-profile outages across several major cloud providers, costing companies around the world billions of dollars. Building resilient systems has never been more of a priority, especially as we move into the era of agentic AI.

Modern applications can easily include hundreds of discrete services, all of which need to be reliable in order for the application to function correctly. While running tests on a handful of critical services can lead to small reliability improvements, real impact requires testing and increased reliability visibility across your entire organization. That’s the logic behind the new, improved Reliability Reports within Gremlin.

On November 18, 2025, X, ChatGPT, Shopify, and many other major sites went offline simultaneously. Even Downdetector, Ookla’s popular outage tracking website, briefly went offline. What caused this issue? Why were so many major websites affected by it? And what steps can you take to reduce the impact on your own applications? ‍

On October 29th, 2025, Azure Front Door suffered an outage that impacted Microsoft services on a global level, including Microsoft 365, Outlook, Xbox Live, Copilot, and more. It also affected Microsoft Azure, meaning companies like Costco, Starbucks, and Alaska Airlines ran into issues for both customer-facing and internal systems. The root of the issue was a misconfiguration in the data plane for Azure Front Door and the Azure Content Delivery Network.

It’s now easier than ever to start testing Kubernetes with Dynatrace and Gremlin. With a new strategic integration, Kubernetes services set up in Dynatrace are automatically discovered in Gremlin to make testing set up simple and fast. At a time when AI is driving massive expansions in infrastructure and dramatically increasing deployment speed, being able to set up and test new services quickly is more important than ever. ‍

On October 19th and 20th, 2025, the AWS region US-EAST-1 suffered a massive outage. What started with a 3-hour Amazon DynamoDB outage from a DNS issue led to an Amazon EC2 outage that lasted an additional 12 hours before normal service was restored. Over the course of the outage, there were over 17 million outage reports as companies like Snapchat, Roblox, Amazon, Reddit, Venmo, and more were impacted.

Point of Sale (POS) systems are the backbone of any retail store. A single outage can cost retail companies thousands of dollars each minute in lost sales, and even more if the outage happens during peak hours. If the outage goes on too long, it can cause even more costly damage as customers abandon carts and turn to competitors. In an industry where customer loyalty is worth its weight in gold, that brand damage can end up even more costly than the initial lost sales.

Over the years, Chaos Engineering has proven its effectiveness time and time again, uncovering risks and saving companies millions they would have lost in painful, brand-impacting outages. But as Chaos Engineering adoption increased, we found organizations running into the same stumbling blocks when they tried to scale. Individual teams would get great results with Chaos Engineering, then stall as they tried to get more teams involved.